Machine Learning to Optimize Busulfan Exposure and Predict Survival in HSCT: Toward Personalized Dosing Strategies
This study demonstrates that machine learning models integrating clinical and pharmacokinetic variables outperform traditional methods in predicting busulfan exposure and survival, thereby enabling more precise, personalized dosing strategies for patients undergoing hematopoietic stem cell transplantation.